Prediction of Severity after Lung Cancer Surgery

Abstract

Operative mortality rates are a problem of great interest among surgeons, patients, because postoperative complications are the foremost reason for any form of thoracic surgery. The statistical optimization and probabilistic approaches used in the branch of artificial intelligence enables computers to “learn” from previous data and detect complicated patterns in large, noisy, or complex data sets. There are many machine learning methods used to predict the mortality of a patient after the lung cancer surgery. The data is collected from patients who underwent major surgeries like Heart Transplant, Lung Transplant and removal of parts of the lungs full of the cancer, this data is used as reference to predict the risk to the patient after the surgery. In this project the overall analysis is done by taking the patient's past medical records, daily habits and predicts outcomes based on records from previous year's surgeries. So, in our project we are building two models using Random Forest, SVM and then the model with best accuracy is used to predict the severity of patient after lung cancer surgery. This outcome will help the doctors to guide the patient on whether to have surgery or not. If doctors believe the surgery may impair the patient's quality of life and there is a known high probability of death within a year, then both parties can decide whether to follow through on surgery or decide an alternative treatment method. So, we will classify the post-operative life span of a patient into two classes i.e., high risk factor with chance of death after surgery and the other one is survival. Here, Random Forest, SVM, and Logistic Regression are used to predict the risk factor.